基于贝叶斯网的电力系统故障诊断方法研究
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摘要
电力系统运行中,时常经受各种自然和人为的干扰,故障的发生难以避免。电网发生故障时,会有大量的信息涌入调度控制中心,给调度人员的事故后处理造成了极大困难,一个自动化的警报处理系统(即故障诊断系统),能为调度人员提供决策支持,并据此对系统进行事故后的快速恢复,减少经济损失。对警报处理的研究起步较早,提出了很多方法,主要集中在如何处理警报信息空间上的不确定性以及如何对大规模电网进行建模,对警报信息的时序特性的考虑较少,而且缺乏一个统一的模型能够同时处理警报信息的时间和空间上的不确定性。
大型电网互联提高了资源优化配置,带来了巨大的经济效益,同时也使得电力系统更容易遭受大规模停电事故的侵害,有研究表明电力系统75%的大干扰与继电保护的不正确动作有关。随着广域通信网络的快速发展,提出了广域后备保护(Wide-area Backup Protection)的概念,以提供更好的选择性、实时性和可靠性。要实现尽快以及尽可能小范围的切除故障元件,完成更好的后备保护功能,首要任务就是快速准确地找出故障元件。广域后备保护的研究,主要集中在系统架构、协作机制等方面,人工智能的故障诊断方法在后备保护中的应用研究尚不多见。
本文首先介绍了警报处理所涉及的故障诊断方法的研究现状,以及广域后备保护算法所涉及的故障诊断方法的研究现状。
接着介绍了贝叶斯网研究中的一些基本概念,阐述了条件独立性在贝叶斯网中的重要性,以及Leaky Noisy-Or模型是如何通过其因果独立机制来降低建模的复杂度,并对贝叶斯网的推理问题及其推理算法进行了介绍。
研究了广域分布式电网故障诊断方法。对元件故障、保护拒动和误动的先验概率获取问题进行了讨论,提出了一种事件采样的先验概率计算方法。根据广域后备保护对故障诊断的实时性和准确性的要求,采用保护测量信号作为诊断证据,提出了一种基于叶斯网动态建模的分布式电网故障诊断方法,以降低诊断建模和推理计算的复杂度。
提出了时间因果贝叶斯网模型,解决一类警报具有时序特性的故障诊断问题。讨论了时间囚果关系中所存在的时间不确定性,引入模糊集理论来表达故障与警报间的时间因果关系,建立了两种最为常见的节点模型——“与”节点和“或”节点,并给出了节点的条件概率以及故障假说的概率的计算方法,最后通过算例演示了时间因果贝叶斯网模型进行故障诊断的推理计算过程,通过算例间的比较说明了模型的准确性。
研究了基于SCADA和SOE信息的电网故障方法(用于调度控制中心的警报处埋)。讨论了SOE信息(保护和断路器动作的时序信息)的加入对提高电网故障诊断准确率的重要性,提出使用时间因果贝叶斯网模型进行电网故障诊断。在时间因果贝叶斯网模型中引入一个新的元素——开关,然后对动态关联路径的概念进行了改进,提出一种基于广度优先搜索的方法寻找关联路径,用以确定开关的状态。最后给出了算例进行验证。
The power system often suffers natural and artificial disturbances during operation, so malfunction is inevitab. When a malfunction occurs, lots of information will emerge into the dispatching control center and make it difficult for the scheduling person to deal with the malfunction. An automatic alarm processing system, namely fault diagnosis system, can give the scheduling person to make decisions and help the system to recover quickly. Studies on alarm processing began early and many methods have been found on how to deal with the alarm information's uncertainty in space. However, temporal information is not given enough attention and we lack a uniform model which can handle spatial and temporal uncertainty at the same time.
Large-scale interconnection of power grids improve the optimization of resources and bring huge economic benefits. Howerver, it makes the power system more accessible to balckouts. A study by NERC shows that about 75 percent of the power system's major disturbances are caused by the incorrrect activities of the relay protection. As the communication network develops quickly, a concept of Wide-area Backup Protection is put forward to offer better effectiveness and reliability. The primary task of communication network is to find out the fault component quickly. Studies on Wide-area Backup Protection focus on system structure and protective strategy and collaboration, not on fault diagnosis.
In this paper, the current situation of fault diagnosis studies connected with alarm processing as well as Wide-area Backup Protection is introduced.
Then the basic concepts on Bayesian Network studies are presented and the importance of conditional independence is elaborated. The paper also expounds on how Leaky Noisy-Or model to lower the complexity of building models through causal independence, as well as the reasoning of Bayesian Network.
Regarding fault diagosis on Wide-area Backup Protection, we discuss on component breakdown, failure operation protection and Prior probability of False operation and put forward a Prior probability calculating method of event sampling. According to Wide-area Backup Protection's requirement for real-time and accuracy, we adopt relay protection measuring signal as diagnosing evidence and bring forward a Bayesian Network-based distributed power grid's fault diagnosing method to reduce the complexity of inference calculation.
As to fault diagnosis on temporal information alarm, we bring forward Temporal Causal Bayesian Network Model in this paper, discuss time uncertainty in causality and adopt fuzzy set theory to express the temporal causality between fault and alarm. We also set up two common node models and give calculating methods on the node's conditional probability and fault hypothesis.
We study power system's fault method based on SCADA and SOE information, discuss the importance of SOE information for enhancing fault diagnosis accuracy of power grids and put forward Temporal Causal Bayesian Network Model for fault diagnosis. A new element-switch, is introduced to Temporal Causal Bayesian Network Model and the concept of dynamic realted path is improved. A mothod based on breadth-first search is adopted to look for related path to determine the status of the swith.
大型电网互联提高了资源优化配置,带来了巨大的经济效益,同时也使得电力系统更容易遭受大规模停电事故的侵害,有研究表明电力系统75%的大干扰与继电保护的不正确动作有关。随着广域通信网络的快速发展,提出了广域后备保护(Wide-area Backup Protection)的概念,以提供更好的选择性、实时性和可靠性。要实现尽快以及尽可能小范围的切除故障元件,完成更好的后备保护功能,首要任务就是快速准确地找出故障元件。广域后备保护的研究,主要集中在系统架构、协作机制等方面,人工智能的故障诊断方法在后备保护中的应用研究尚不多见。
本文首先介绍了警报处理所涉及的故障诊断方法的研究现状,以及广域后备保护算法所涉及的故障诊断方法的研究现状。
接着介绍了贝叶斯网研究中的一些基本概念,阐述了条件独立性在贝叶斯网中的重要性,以及Leaky Noisy-Or模型是如何通过其因果独立机制来降低建模的复杂度,并对贝叶斯网的推理问题及其推理算法进行了介绍。
研究了广域分布式电网故障诊断方法。对元件故障、保护拒动和误动的先验概率获取问题进行了讨论,提出了一种事件采样的先验概率计算方法。根据广域后备保护对故障诊断的实时性和准确性的要求,采用保护测量信号作为诊断证据,提出了一种基于叶斯网动态建模的分布式电网故障诊断方法,以降低诊断建模和推理计算的复杂度。
提出了时间因果贝叶斯网模型,解决一类警报具有时序特性的故障诊断问题。讨论了时间囚果关系中所存在的时间不确定性,引入模糊集理论来表达故障与警报间的时间因果关系,建立了两种最为常见的节点模型——“与”节点和“或”节点,并给出了节点的条件概率以及故障假说的概率的计算方法,最后通过算例演示了时间因果贝叶斯网模型进行故障诊断的推理计算过程,通过算例间的比较说明了模型的准确性。
研究了基于SCADA和SOE信息的电网故障方法(用于调度控制中心的警报处埋)。讨论了SOE信息(保护和断路器动作的时序信息)的加入对提高电网故障诊断准确率的重要性,提出使用时间因果贝叶斯网模型进行电网故障诊断。在时间因果贝叶斯网模型中引入一个新的元素——开关,然后对动态关联路径的概念进行了改进,提出一种基于广度优先搜索的方法寻找关联路径,用以确定开关的状态。最后给出了算例进行验证。
The power system often suffers natural and artificial disturbances during operation, so malfunction is inevitab. When a malfunction occurs, lots of information will emerge into the dispatching control center and make it difficult for the scheduling person to deal with the malfunction. An automatic alarm processing system, namely fault diagnosis system, can give the scheduling person to make decisions and help the system to recover quickly. Studies on alarm processing began early and many methods have been found on how to deal with the alarm information's uncertainty in space. However, temporal information is not given enough attention and we lack a uniform model which can handle spatial and temporal uncertainty at the same time.
Large-scale interconnection of power grids improve the optimization of resources and bring huge economic benefits. Howerver, it makes the power system more accessible to balckouts. A study by NERC shows that about 75 percent of the power system's major disturbances are caused by the incorrrect activities of the relay protection. As the communication network develops quickly, a concept of Wide-area Backup Protection is put forward to offer better effectiveness and reliability. The primary task of communication network is to find out the fault component quickly. Studies on Wide-area Backup Protection focus on system structure and protective strategy and collaboration, not on fault diagnosis.
In this paper, the current situation of fault diagnosis studies connected with alarm processing as well as Wide-area Backup Protection is introduced.
Then the basic concepts on Bayesian Network studies are presented and the importance of conditional independence is elaborated. The paper also expounds on how Leaky Noisy-Or model to lower the complexity of building models through causal independence, as well as the reasoning of Bayesian Network.
Regarding fault diagosis on Wide-area Backup Protection, we discuss on component breakdown, failure operation protection and Prior probability of False operation and put forward a Prior probability calculating method of event sampling. According to Wide-area Backup Protection's requirement for real-time and accuracy, we adopt relay protection measuring signal as diagnosing evidence and bring forward a Bayesian Network-based distributed power grid's fault diagnosing method to reduce the complexity of inference calculation.
As to fault diagnosis on temporal information alarm, we bring forward Temporal Causal Bayesian Network Model in this paper, discuss time uncertainty in causality and adopt fuzzy set theory to express the temporal causality between fault and alarm. We also set up two common node models and give calculating methods on the node's conditional probability and fault hypothesis.
We study power system's fault method based on SCADA and SOE information, discuss the importance of SOE information for enhancing fault diagnosis accuracy of power grids and put forward Temporal Causal Bayesian Network Model for fault diagnosis. A new element-switch, is introduced to Temporal Causal Bayesian Network Model and the concept of dynamic realted path is improved. A mothod based on breadth-first search is adopted to look for related path to determine the status of the swith.
引文
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